ChatGPT: LOOKING FOR A CAFFEINE SUBSTITUTE
Although caffeine stimulates mental activity and aids in eSports/games and late-night programming, it has many side effects, such as increased blood pressure and crazy heart rate, a sharp rise and quick drop in stimulation. Therefore, we need a high-quality alternative to caffeine, and we will search for it using AI. Potential candidates to replace caffeine are Theacrine (or TeaCrine) and N-Phenethyldimethylamine Citrate (USA FDA said Ok).
ChatGPT successfully created a very complex table, even with a calculated column based on FUZZY criteria (if you can do this in SQL — you're a genius!), but it struggled with sorting the table. Attention: there is an image below, links are not clickable.
In the first numeric column, it failed to sort the numbers in descending order. I spent about 15-20 minutes trying. I experimented with various prompts and explanations. This is strange.
This tool (ChatGPT) understands table manipulation commands very well. In this example, I asked it to create a table based on data from large stores, specified which columns were needed and what information they should contain, indicated the order of the columns, including relative positioning — for instance, "insert a column with such-and-such data before this column" — and even more.
IT was able to create a SUMMARY column based on previously generated columns — this is the column with weighted sums of substance weights from other columns, and IT independently found the weighting coefficients quite accurately.
Moreover, for each product, IT managed to identify the substance composition based on specific criteria and listed them, creating a separate column. Not all substances, but only those filtered by certain criteria (only those that are not caffeine but have an effect similar to caffeine — try programming such a query in SQL manually without AI, taking into account the fuzzy criterion of similarity of effects, and also determine the similarity coefficient for creating the weighted sum of substance masses per serving of the dietary supplement). And it even partially managed to sort by the weighted sum.
But despite completing so much complex work, it still made a small mistake with sorting.